We consider three different classifiers that are tuned by the optimization algorithms proposed in Section : The support vector machine classifier (SVM) [1]} with Gaussian kernel, extreme gradient boosting (xgboost) [2]} and the kernelized k-nearest-neighbor classifier (kknn) [3]}. We decided to consider these learners because they represent three very distinct learning paradigms, and because they are generally regarded responsive to hyperparameter tuning. The hyperparameter spaces that are being tuned over are presented in Table REF . They also react differently to high dimensionality: kknn is vulnerable to the “curse of dimensionality”, the SVM is a regularized modeling algorithm, and the xgboost algorithm does implicit feature selection because it is a tree-based learner.
<TABLE>